Sürdürülebilir Kalkınma Amaçları

• 2
• 1
• 1

Yazarlar

• Araz, Onur(1)
• Aslan, Muhammed Emin(1)
• Bayram, Bülent(1)
• Bozkurt, Salih(1)
• Büyük, Ersin(1)
• Cakir, Tufan(1)
• Catal Reis, Hatice(1)
• Demir, Nusret(1)
• Ince, Abdulkadir(1)
• Işık, Buket Özdemir(1)
• Kalfa, Eren(1)
• Karaman, Abdullah(1)
• Ozturk, Kasif Furkan(1)
• Reis, Hatice Catal(1)
• Sarıhan, Köksal(1)
• Seker, Dursun Zafer(1)
• Topçu, Osman(1)
• Turk, Veysel(1)
• Türe, Hayati(1)
• Özdemir, Erhan(1)

• MakaleAçık Erişim2026Scopus SJRQ2/r/gumushane/12307
  A Personalized Target Placement Optimization Framework for VR-Based Upper Extremity Rehabilitation

  Türe, Hayati; Kalfa, Eren; Aslan, Muhammed Emin; Işık, Buket Özdemir; Topçu, Osman; Özdemir, Erhan; Sarıhan, Köksal

  Featured Application: The proposed framework can be directly integrated into existing VR rehabilitation platforms to provide real-time personalized target placement for post-stroke upper extremity therapy, requiring no additional hardware beyond a standard CPU. Virtual reality (VR)-based rehabilitation is an established modality for upper extremity motor recovery; however, existing systems frequently rely on fixed, random, or therapist-tuned target placement that disregards patient-specific motor capacity and population-level priors. This study proposes a cross-patient collaborative swarm intelligence framework that derives zone-based patient profiles from real VR trajectories and augments them with a similarity-weighted cohort prior distilled from clinically similar patients’ successful trajectory clouds and zone-transition graphs. A hybrid Ant Colony Optimization (ACO)–Particle Swarm Optimization (PSO) algorithm optimizes 12 targets per session across a 27-zone ( (Formula presented.) ) workspace using a five-component fitness function encompassing reachability, zone balance, movement efficiency, heatmap-guided challenge coverage, and swarm-flow consistency. The framework was evaluated retrospectively on a single-center cohort of 36 post-stroke patients and 6373 sessions under a leakage-safe simulation protocol with 70/30 chronological splits; outcomes are model-based proxy success rates derived from each patient’s profile rather than directly observed task success. The hybrid strategy achieved a mean simulated success rate of 85.5% ± 5.5%, a 36.4% relative improvement over random placement (Wilcoxon (Formula presented.), Cohen’s (Formula presented.) ); the leakage-safe split yielded 80.1% on the held-out segment versus 61.1% for random, with no statistically significant train–test gap ( (Formula presented.) ). Ablation confirmed both PSO and ACO are individually necessary ( (Formula presented.) pp, (Formula presented.) ). Total session-start computation is 78 ms on standard CPU hardware. These findings constitute a proof-of-concept that collaborative personalized swarm optimization can substantially outperform heuristic target placement under in silico evaluation; clinical efficacy in terms of standardized motor outcome measures remains to be established in a prospective randomized controlled trial, and the findings should be replicated across centers, task modes, and a larger cohort before generalization.

  • 
  İncele
• MakaleYalnız künye2024Scopus SJRQ1/r/gumushane/14403
  Caprock integrity at Çanakkale-Tuzla hydrothermal system inferred from magnetotelluric modeling using particle swarm optimization

  Büyük, Ersin; Karaman, Abdullah

  The impermeable caprock within a geothermal system serves the purpose of effectively sealing the reservoir, resulting in an elevation of both pressure and temperature. This sealing mechanism plays a crucial role in the long-term preservation of the system while also contributing to its overall sustainability. Caprock failure subsequent to seismic activity near a geothermal site can lead to the permeation of the caprock structure, resulting in diminished sealing capabilities and a decline in the reservoir temperature. In addition, this process alters the geochemical composition of the water by creating a hydrothermal mixture zone that disrupts the resistivity structure of the caprock, which is typically characterized by low resistivity values due to its substantial clay content and mineral alteration. This study focuses on investigating the integrity of the caprock at the Çanakkale-Tuzla geothermal field in Turkey, where water temperature and conductivity were reported to have decreased after a moderate-magnitude earthquake and subsequent aftershocks. For this purpose, we have performed magnetotelluric (MT) measurements, a method known for its sensitivity to geochemical reactions. These measurements are conducted along two parallel profiles that encompassed a total of 32 stations. The particle swarm optimization (PSO) technique is used to overcome the subtle difficulties associated with conventional inversion methods in modeling the MT data of complex formations. This is the first study that overcomes the difficulties emanating from the caprock failure by modeling MT data using PSO. Our modeling approach produces resistivity images that we interpreted as the signature of the failed caprock following the earthquake at the study site. Our results appear to confirm the documented geochemical changes, or hydrothermal mixture zone around the caprock structure.

  • 
  AtıfScopus5·WoS6·OpenAlex2

  İncele
• MakaleAçık Erişim2023/r/gumushane/14401
  Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database

  Reis, Hatice Catal; Turk, Veysel

  Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning techniques can be used to reduce the workload of experts and to produce quick solutions. The nuclei found in the histopathology dataset are an essential parameter in disease detection. The nucleus segmentation was performed using the colorectal histology MNIST dataset for nucleus detection in this study. The graph theory, PSO, watershed, and random walker algorithms were used for the segmentation process. In addition, we present the 10-class MedCLNet visual dataset consisting of the NCT-CRC-HE-100 K dataset, LC25000 dataset, and GlaS dataset that can be used in transfer learning studies from deep learning techniques. The study proposes a transfer learning technique using the MedCLNet database. Deep neural networks pre-trained with the proposed transfer learning method were used in the classification with the colorectal histology MNIST dataset in the experimental process. DenseNet201, DenseNet169, InceptionResNetV2, InceptionV3, ResNet152V2, ResNet101V2, and Xception deep learning algorithms were used in transfer learning and the classification studies. The proposed approach was analyzed before and after transfer learning with different methods (DenseNet169 + SVM, DenseNet169 + GRU). In the performance measurement, using the colorectal histology MNIST dataset, 94.29% accuracy was obtained in the DenseNet169 model, which was initiated with random weights in the multi-classification study, and 95.00% accuracy after transfer learning was applied. In comparison with the results obtained from empirical studies, it was demonstrated that the proposed method produced satisfactory outcomes. The application is expected to provide a secondary evaluation for physicians in colon cancer detection and the segmentation. Graphical Abstract: [Figure not available: see fulltext.].

  • 
  AtıfScopus29·WoS21·OpenAlex37

  İncele
• MakaleYalnız künye2022Scopus SJRQ2/r/gumushane/11420
  Effect of different objective functions on control performance of tuned mass damper for a high-rise building considering soil–structure interaction

  Araz, Onur; Ozturk, Kasif Furkan; Cakir, Tufan

  This paper deals with the optimization of the tuned mass damper (TMD) with different objective functions for high-rise building under earthquake motions considering soil–structure interaction (SSI). A study is conducted to search for the TMD that outperforms five objective functions (i.e., OF-1–OF-5) containing combinations of the structural responses and foundation responses. The main goal of the present study is to evaluate and compare the control performance of these five objective functions. The optimum parameters of TMD are obtained by particle swarm algorithm (PSA) to minimize the acceleration transfer function. To determine the dynamic response of a 40-story building under the influence of SSI, six different foundation soil conditions and eight different earthquake records are considered. The results show that the SSI effects on the dynamic response of the high-rise building resting on relatively soft soil types such as SD and SE are of critical importance. It is shown that the effectiveness of the optimum TMD designed by OF-1 is better than the others in suppressing the peak response values for all soil types. After OF-1, the best performances are obtained for OF-3 and OF-5 which yield approximately the same effectiveness. It is also found that different earthquake records, soil types and objective functions are very effective on the effectiveness of TMD.

  • 
  AtıfScopus22·WoS22·OpenAlex25

  İncele
• MakaleYalnız künye2018Scopus SJRQ3/r/gumushane/13831
  An Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT Imagery

  Catal Reis, Hatice; Bayram, Bülent; Bozkurt, Salih; Ince, Abdulkadir; Demir, Nusret; Seker, Dursun Zafer

  This study presents an extended shoreline detection approach from pansharpened images of Turkish RASAT satellite which covers red, green and blue bands of the optical spectrum with 15 m ground resolution and panchromatic band with 7.5 m spatial resolution. The Lake Ercek of Turkey has been selected as the study area, which is a tectonic lake and home to a variety of water birds. The satellite images of the lake taken in 2013 and 2014 were considered for analysis. The proposed shoreline extraction system consists of a sequence of image processing steps in which simple linear clustering (SLIC) and particle swarm optimization (PSO) are the main components. SLIC was used to create superpixels that form basis for object-based image analysis while PSO was employed for classifying objects into corresponding classes. The resulting images still contained unwanted artefacts; therefore, a post-processing step was performed to improve the accuracy of segmentation by applying thresholding, morphological processing, and manual editing for noise removal. The proposed framework was applied on two temporal RASAT images to test the variations of defined parameter settings. The success of the proposed system was to obtain shorelines with satisfying accuracy without using NIR band. Finally, the extracted shorelines were vectorised and compared with manually digitized shorelines from pansharpened satellite images for accuracy assessment.

  AtıfScopus3·WoS2·OpenAlex2

  İncele